SoroushVahidi/scidocs huggingface dataset:
Efficient Realization of Coordinate Structures in Combinatory Categorial Grammar
(2006)

We describe a chart realization algorithm for Combinatory Categorial Grammar (CCG), and show how it can be used to efficiently realize a wide range of coordination phenomena, including argument cluster coordination and gapping. The algorithm incorporates three novel methods for improving the efficiency of chart realization: (i) using rules to chunk the input logical form into sub-problems to be solved independently prior to further combination; (ii) pruning edges from the chart based on the n-gram score of the edge’s string, in comparison to other edges with equivalent categories; and (iii) formulating the search as a best-first anytime algorithm, using n-gram scores to sort the edges on the agenda. The algorithm has been implemented as an extension to the OpenCCG open source CCG parser, and initial performance tests indicate that the realizer is fast enough for practical use in natural language dialogue systems.
Statistical language modeling using the CMU-cambridge toolkit
(1997)

The CMU Statistical Language Modeling toolkit was re leased in in order to facilitate the construction and testing of bigram and trigram language models It is currently in use in over academic government and industrial laboratories in over countries This paper presents a new version of the toolkit We outline the con ventional language modeling technology as implemented in the toolkit and describe the extra e ciency and func tionality that the new toolkit provides as compared to previous software for this task Finally we give an exam ple of the use of the toolkit in constructing and testing a simple language model
A source code linearization technique for detecting plagiarized programs
(2007)

It is very important to detect plagiarized programs in the field of computer science education. Therefore, many tools and algorithms have been developed for this purpose. Generally, these tools are operated in two phases. In phase 1, a program plagiarism detecting tool generates an intermediate representation from a given program set. The intermediate representation should reflect the structural characterization of the program. Most tools use the parse tree or token sequence by intermediate representation. In phase 2, the program looks for plagiarized material and evaluates the similarity of two programs. It is helpful to announce the plagiarized metarials between two programs to the instructor. In this paper, we present the static tracing method in order to improve program plagiarism detection accuracy. The static tracing method statically executes a program at the syntax-level and then extracts predefined keywords according to the order of the executed functions. The result of experiment proves this method can detect plagiarism more effectively than the previously released plagiarism detecting method.
Silicon-Filled Rectangular Waveguides and Frequency Scanning Antennas for mm-Wave Integrated Systems
(2013)

We present a technology for the manufacturing of silicon-filled integrated waveguides enabling the realization of low-loss high-performance millimeter-wave passive components and high gain array antennas, thus facilitating the realization of highly integrated millimeter-wave systems. The proposed technology employs deep reactive-ion-etching (DRIE) techniques with aluminum metallization steps to integrate rectangular waveguides with high geometrical accuracy and continuous metallic side walls. Measurement results of integrated rectangular waveguides are reported exhibiting losses of 0.15 dB/ λg at 105 GHz. Moreover, ultra-wideband coplanar to waveguide transitions with 0.6 dB insertion loss at 105 GHz and return loss better than 15 dB from 80 to 110 GHz are described and characterized. The design, integration and measured performance of a frequency scanning slotted-waveguide array antenna is reported, achieving a measured beam steering capability of 82 ° within a band of 23 GHz and a half-power beam-width (HPBW) of 8.5 ° at 96 GHz. Finally, to showcase the capability of this technology to facilitate low-cost mm-wave system level integration, a frequency modulated continuous wave (FMCW) transmit-receive IC for imaging radar applications is flip-chip mounted directly on the integrated array and experimentally characterized.
Development of a continuum robot using pneumatic artificial muscles
(2014)

This paper presents the design concept of an intrinsic spatial continuum robot, of which arm segments are actuated by pneumatic artificial muscles. Since pneumatic artificial muscles supported the arm segments of the robot instead of a passive spring, the developed continuum robot was not only capable of following a given trajectory, but also varying its stiffness according to given external environment. Experiment results revealed that the proposed continuum robot showed versatile movements by controlling the pressure of supply air to pneumatic artificial muscles, and the well-coordinated pattern matching scheme based on low-cost webcams gave good performance in estimating the position and orientation of the end-effector of the spatial continuum robot.
Introduction of speech log-spectral priors into dereverberation based on Itakura-Saito distance minimization
(2012)

It has recently been shown that a multi-channel linear prediction can effectively achieve blind speech dereverberation based on maximum-likelihood (ML) estimation. This approach can estimate and cancel unknown reverberation processes from only a few seconds of observation. However, one problem with this approach is that speech distortion may increase if we iterate the dereverberation more than once based on Itakura-Saito (IS) distance minimization to further reduce the reverberation. To overcome this problem, we introduce speech log-spectral priors into this approach, and reformulate it based on maximum a posteriori (MAP) estimation. Two types of priors are introduced, a Gaussian mixture model (GMM) of speech log spectra, and a GMM of speech mel-frequency cepstral coefficients. In the formulation, we also propose a new versatile technique to integrate such log-spectral priors with the IS distance minimization in a computationally efficient manner. Preliminary experiments show the effectiveness of the proposed approach.
Disentangled Representation Learning for Text Style Transfer
(2018)

This paper tackles the problem of disentangling the latent variables of style and content in language models. We propose a simple, yet effective approach, which incorporates auxiliary objectives: a multi-task classification objective, and dual adversarial objectives for label prediction and bag-ofwords prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space, using this approach. This disentangled latent representation learning method is applied to attribute (e.g. style) transfer on non-parallel corpora. We achieve similar content preservation scores compared to previous state-of-the-art approaches, and significantly better style-transfer strength scores. Our code is made publicly available for replicability and extension purposes .
Abstractive Multi-document Summarization with Semantic Information Extraction
(2015)

This paper proposes a novel approach to generate abstractive summary for multiple documents by extracting semantic information from texts. The concept of Basic Semantic Unit (BSU) is defined to describe the semantics of an event or action. A semantic link network on BSUs is constructed to capture the semantic information of texts. Summary structure is planned with sentences generated based on the semantic link network. Experiments demonstrate that the approach is effective in generating informative, coherent and compact summary.
Opportunistic Computation Offloading in Mobile Edge Cloud Computing Environments
(2016)

The dynamic mobility and limitations in computational power, battery resources, and memory availability are main bottlenecks in fully harnessing mobile devices as data mining platforms. Therefore, the mobile devices are augmented with cloud resources in mobile edge cloud computing (MECC) environments to seamlessly execute data mining tasks. The MECC infrastructures provide compute, network, and storage services in one-hop wireless distance from mobile devices to minimize the latency in communication as well as provide localized computations to reduce the burden on federated cloud systems. However, when and how to offload the computation is a hard problem. In this paper, we present an opportunistic computation offloading scheme to efficiently execute data mining tasks in MECC environments. The scheme provides the suitable execution mode after analyzing the amount of unprocessed data, privacy configurations, contextual information, and available on-board local resources (memory, CPU, and battery power). We develop a mobile application for online activity recognition and evaluate the proposed scheme using the event data stream of 5 million activities collected from 12 users for 15 days. The experiments show significant improvement in execution time and battery power consumption resulting in 98% data reduction.
Large-scale audio feature extraction and SVM for acoustic scene classification
(2013)

This work describes a system for acoustic scene classification using large-scale audio feature extraction. It is our contribution to the Scene Classification track of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (D-CASE). The system classifies 30 second long recordings of 10 different acoustic scenes. From the highly variable recordings, a large number of spectral, cepstral, energy and voicing-related audio features are extracted. Using a sliding window approach, classification is performed on short windows. SVM are used to classify these short segments, and a majority voting scheme is employed to get a decision for longer recordings. On the official development set of the challenge, an accuracy of 73 % is achieved. SVM are compared with a nearest neighbour classifier and an approach called Latent Perceptual Indexing, whereby SVM achieve the best results. A feature analysis using the t-statistic shows that mainly Mel spectra are the most relevant features.
A fractional order fuzzy PID controller for binary distillation column control
(2015)

Expert and intelligent control schemes have recently emerged out as a promising solution with robustness which can efficiently deal with the nonlinearities, along with various types of modelling uncertainties, present in different real world systems e.g. binary distillation column. This paper is an attempt to propose an intelligent control system which takes the form of a Fractional Order Fuzzy Proportional – Integral – Derivative (FOFPID) controller which is investigated as a solution to deal with the complex dynamic nature of the distillation column. The FOFPID controller is an extension of an existing formula based self tuning fuzzy Proportional Integral controller structure, which varies its gains at run time in accordance with the instantaneous error and rate of change of error. The FOFPID controller is a Takagi-Sugeno (TS) model based fuzzy adaptive controller comprising of non-integer order of integration and differentiation operators used in the controller. It has been observed that inclusion of non-integer order of the integration and differentiation operators made the controller scheme more robust. For the performance evaluation of the proposed scheme, the performance of FOFPID controller is compared with that of its integer order counterpart, a Fuzzy Proportional – Integral – Derivative (FPID) controller. The parameters of both the controllers were optimized for minimum integral of absolute error (IAE) using a bio-inspired global optimization algorithm, Genetic Algorithm (GA). Intensive LabVIEW TM simulation studies were performed which included setpoint tracking with and without uncertainties, disturbance rejection, and noise suppression investigations. For testing the parameter uncertainty handling capability of the proposed controller, uncertain and time varying relative volatility and uncertain tray hydraulic constant were applied. Also, for the disturbance rejection studies, intensive simulations were conducted, which included two most common causes of disturbance i.e. variation in feed composition and variation in feed flow rate. All the simulation investigations clearly suggested that FOFPID controller provided superior performance over FPID controller for each case study i.e. setpoint tracking, disturbance rejection, noise suppression and parameter uncertainties.
A three-component scattering model for polarimetric SAR data
(1998)

An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations. The mechanisms are canopy scatter from a cloud of randomly oriented dipoles, evenor double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants and Bragg scatter from a moderately rough surface. This composite scattering model is used to describe the polarimetric backscatter from naturally occurring scatterers. The model is shown to describe the behavior of polarimetric backscatter from tropical rain forests quite well by applying it to data from NASA/Jet Propulsion Laboratory’s (JPL’s) airborne polarimetric synthetic aperture radar (AIRSAR) system. The model fit allows clear discrimination between flooded and nonflooded forest and between forested and deforested areas, for example. The model is also shown to be usable as a predictive tool to estimate the effects of forest inundation and disturbance on the fully polarimetric radar signature. An advantage of this model fit approach is that the scattering contributions from the three basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. Furthermore, it is shown that the contributions of the three scattering mechanisms to the HH, HV, and VV backscatter can be calculated from the model fit. Finally, this model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.
Stacked Patch Antenna With Dual-Polarization and Low Mutual Coupling for Massive MIMO
(2016)

Massive multiple input and multiple output (MIMO) has attracted significant interests in both academia and industry. It has been considered as one of most promising technologies for 5G wireless systems. The large-scale antenna array for base stations naturally becomes the key to deploy the Massive MIMO technologies. In this communication, we present a dual-polarized antenna array with 144 ports for Massive MIMO operating at 3.7 GHz. The proposed array consists of 18 low profile subarrays. Each subarray consists of four single units. Each single antenna unit consists of one vertically polarized port and one horizontally polarized port connected to power splitters, which serve as a feeding network. A stacked patch design is used to construct the single unit with the feeding network, which gives higher gain and lower mutual coupling within the size of a conversional dual-port patch antenna. Simulation results of the proposed single antenna unit, sub-array, and Massive MIMO array are verified by measurement.
On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals
(2011)

Modern web search engines are federated --- a user query is sent to the numerous specialized search engines called verticals like web (text documents), News, Image, Video, etc. and the results returned by these engines are then aggregated and composed into a search result page (SERP) and presented to the user. For a specific query, multiple verticals could be relevant, which makes the placement of these vertical results within blocks of textual web results challenging: how do we represent, assess, and compare the relevance of these heterogeneous entities?
 In this paper we present a machine-learning framework for SERP composition in the presence of multiple relevant verticals. First, instead of using the traditional label generation method of human judgment guidelines and trained judges, we use a randomized online auditioning system that allows us to evaluate triples of the form query, web block, vertical>. We use a pairwise click preference to evaluate whether the web block or the vertical block had a better users' engagement. Next, we use a hinged feature vector that contains features from the web block to create a common reference frame and augment it with features representing the specific vertical judged by the user. A gradient boosted decision tree is then learned from the training data. For the final composition of the SERP, we place a vertical result at a slot if the score is higher than a computed threshold. The thresholds are algorithmically determined to guarantee specific coverage for verticals at each slot.
 We use correlation of clicks as our offline metric and show that click-preference target has a better correlation than human judgments based models. Furthermore, on online tests for News and Image verticals we show higher user engagement for both head and tail queries.
Soft start-up for high frequency LLC resonant converter with optimal trajectory control
(2015)

This paper investigates the soft start-up for high frequency LLC resonant converter with optimal trajectory control. Two methods are proposed to realize soft start-up for high frequency LLC converter by commercial low-cost microcontrollers (MCU). Both methods can achieve soft start-up with minimum stress and optimal energy delivery. One method is mixed-signal implementation by sensing resonant tank to minimize the digital delay. Another method is digital implementation with look-up table. Experimental results are demonstrated on 500kHz 1kW 400V/12V LLC converter.
Grammar Variational Autoencoder
(2017)

s m i l e s ! c h a i n atom ! b r a c k e t _ a t o m | a l i p h a t i c _ o r g a n i c | a r o m a t i c _ o r g a n i c a l i p h a t i c _ o r g a n i c ! ’B’ | ’C’ | ’N’ | ’O’ | ’S ’ | ’P ’ | ’F ’ | ’ I ’ | ’ Cl ’ | ’ Br ’ a r o m a t i c _ o r g a n i c ! ’ c ’ | ’ n ’ | ’ o ’ | ’ s ’ b r a c k e t _ a t o m ! ’ [ ’ BAI ’ ] ’ BAI ! i s o t o p e symbol BAC | symbol BAC | i s o t o p e symbol | symbol BAC ! c h i r a l BAH | BAH | c h i r a l BAH ! hc ou n t BACH | BACH | h co u n t BACH ! c h a r g e c l a s s | c h a r g e | c l a s s symbol ! a l i p h a t i c _ o r g a n i c | a r o m a t i c _ o r g a n i c i s o t o p e ! DIGIT | DIGIT DIGIT | DIGIT DIGIT DIGIT DIGIT ! ’1 ’ | ’2 ’ | ’3 ’ | ’4 ’ | ’5 ’ | ’6 ’ | ’7 ’ | ’8 ’ c h i r a l ! ’@’ | ’@@’ hc ou n t ! ’H’ | ’H’ DIGIT c h a r g e ! ’ ’ | ’ ’ DIGIT | ’ ’ DIGIT DIGIT | ’+ ’ | ’+ ’ DIGIT | ’+ ’ DIGIT DIGIT bond ! ’ ’ | ’= ’ | ’# ’ | ’ / ’ | ’ \ ’ r i n g b o n d ! DIGIT | bond DIGIT branched_a tom ! atom | atom RB | atom BB | atom RB BB RB ! RB r i n g b o n d | r i n g b o n d BB ! BB b ra n ch | b r a n ch b ra nc h ! ’ ( ’ c h a i n ’ ) ’ | ’ ( ’ bond c h a i n ’ ) ’ c h a i n ! branched_a tom | c h a i n branched_a tom | c h a i n bond branched_a tom
Fashion-focused creative commons social dataset
(2013)

In this work, we present a fashion-focused Creative Commons dataset, which is designed to contain a mix of general images as well as a large component of images that are focused on fashion (i.e., relevant to particular clothing items or fashion accessories). The dataset contains 4810 images and related metadata. Furthermore, a ground truth on image's tags is presented. Ground truth generation for large-scale datasets is a necessary but expensive task. Traditional expert based approaches have become an expensive and non-scalable solution. For this reason, we turn to crowdsourcing techniques in order to collect ground truth labels; in particular we make use of the commercial crowdsourcing platform, Amazon Mechanical Turk (AMT). Two different groups of annotators (i.e., trusted annotators known to the authors and crowdsourcing workers on AMT) participated in the ground truth creation. Annotation agreement between the two groups is analyzed. Applications of the dataset in different contexts are discussed. This dataset contributes to research areas such as crowdsourcing for multimedia, multimedia content analysis, and design of systems that can elicit fashion preferences from users.
Memory versus non-linearity in reservoirs
(2010)

Reservoir Computing (RC) is increasingly being used as a conceptually simple yet powerful method for using the temporal processing of recurrent neural networks (RNN). However, because fundamental insight in the exact functionality of the reservoir is as yet still lacking, in practice there is still a lot of manual parameter tweaking or brute-force searching involved in optimizing these systems. In this contribution we aim to enhance the insights into reservoir operation, by experimentally studying the interplay of the two crucial reservoir properties, memory and non-linear mapping. For this, we introduce a novel metric which measures the deviation of the reservoir from a linear regime and use it to define different regions of dynamical behaviour. Next, we study the relationship of two important reservoir parameters, input scaling and spectral radius, on two properties of an artificial task, namely memory and non-linearity.
Improving feature selection techniques for machine learning
(2007)

As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance than the others in many cases. INDEX WORDS: Feature selection, Gene selection, Term selection, Dimension Reduction, Genetic algorithm, Text categorization, Text classification IMPROVING FEATURE SELECTION TECHNIQUES FOR